Article

Segregating variation in the transcriptome: cis regulation and additivity of effects.

School of Integraive Biology, University of Illinois, Urbana, Illinois 61801, USA.
Genetics (Impact Factor: 4.87). 08/2006; 173(3):1347-55. DOI: 10.1534/genetics.105.051474
Source: PubMed

ABSTRACT Properties of genes underlying variation in complex traits are largely unknown, especially for variation that segregates within populations. Here, we evaluate allelic effects, cis and trans regulation, and dominance patterns of transcripts that are genetically variable in a natural population of Drosophila melanogaster. Our results indicate that genetic variation due to the third chromosome causes mainly additive and nearly additive effects on gene expression, that cis and trans effects on gene expression are numerically about equal, and that cis effects account for more genetic variation than do trans effects. We also evaluated patterns of variation in different functional categories and determined that genes involved in metabolic processes are overrepresented among variable transcripts, but those involved in development, transcription regulation, and signal transduction are underrepresented. However, transcripts for proteins known to be involved in protein-protein interactions are proportionally represented among variable transcripts.

Full-text

Available from: Elizabeth Ruedi, Feb 27, 2014
0 Followers
 · 
89 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The mechanistic basis of regulatory variation and the prevailing evolutionary forces shaping that variation are known to differ between sexes and between chromosomes. Regulatory variation of gene expression can be due to functional changes within a gene itself (cis), or in other genes elsewhere in the genome (trans). The evolutionary properties of cis mutations are expected to differ from mutations affecting gene expression in trans. We analyze allele-specific expression across a set of X substitution lines in intact adult Drosophila simulans to evaluate whether regulatory variation differs for cis and trans, for males and females, and for X-linked and autosomal genes. Regulatory variation is common (56% of genes), and patterns of variation within D. simulans are consistent with previous observations in Drosophila that there is more cis than trans variation within species (47% vs. 25%, respectively). The relationship between sex-bias and sex-limited variation is remarkably consistent across sexes. However, there are differences between cis and trans effects: cis variants show evidence of purifying selection in the sex towards which expression is biased, while trans variants do not. For female-biased genes, the X is depleted for trans variation in a manner consistent with a female-dominated selection regime on the X. Surprisingly, there is no evidence for depletion of trans variation for male-biased genes on the X. This is evidence for regulatory feminization of the X, trans acting factors controlling male-biased genes are more likely to be found on the autosomes than those controlling female-biased genes.
    Genome Biology and Evolution 04/2014; 6(4). DOI:10.1093/gbe/evu060 · 4.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Mapping expression quantitative trait loci (eQTL) of targeted genes represents a powerful and widely adopted approach to identify putative regulatory variants. Linking regulation differences to specific genes might assist in the identification of networks and interactions. The objective of this study is to identify eQTL underlying expression of four gene families encoding isoflavone synthetic enzymes involved in the phenylpropanoid pathway, which are phenylalanine ammonia-lyase (PAL; EC 4.3.1.5), chalcone synthase (CHS; EC 2.3.1.74), 2-hydroxyisoflavanone synthase (IFS; EC:1.14.13.136) and flavanone 3-hydroxylase (F3H; EC 1.14.11.9). A population of 130 recombinant inbred lines (F5:11), derived from a cross between soybean cultivar 'Zhongdou 27' (high isoflavone) and 'Jiunong 20' (low isoflavone), and a total of 194 simple sequence repeat (SSR) markers were used in this study. Overlapped loci of eQTLs and phenotypic QTLs (pQTLs) were analyzed to identify the potential candidate genes underlying the accumulation of isoflavone in soybean seed.
    BMC Genomics 08/2014; 15(1):680. DOI:10.1186/1471-2164-15-680 · 4.04 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of allelic imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data. Results Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test. Conclusions To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-920) contains supplementary material, which is available to authorized users.
    BMC Genomics 10/2014; 15(1):920. DOI:10.1186/1471-2164-15-920 · 4.04 Impact Factor